28 citations · 130 across the 32 of their papers we have counts for
4 papers · 1 filter
Clustering Stable Instances of Euclidean k-means
Abhratanu Dutta, Aravindan Vijayaraghavan, Alex Wang
The Euclidean k-means problem is arguably the most widely-studied clustering problem in machine learning. While the k-means objective is NP-hard in the worst-case, practitioners ha…
Clustering Semi-Random Mixtures of Gaussians
Pranjal Awasthi, Aravindan Vijayaraghavan
Gaussian mixture models (GMM) are the most widely used statistical model for the -means clustering problem and form a popular framework for clustering in machine learning and da…
Optimality of Approximate Inference Algorithms on Stable Instances
Hunter Lang, David Sontag, Aravindan Vijayaraghavan
Approximate algorithms for structured prediction problems---such as LP relaxations and the popular alpha-expansion algorithm (Boykov et al. 2001)---typically far exceed their theor…
On Learning Mixtures of Well-Separated Gaussians
Oded Regev, Aravindan Vijayaraghavan
We consider the problem of efficiently learning mixtures of a large number of spherical Gaussians, when the components of the mixture are well separated. In the most basic form of…